An integrated model of clinical information and gene expression for prediction of survival in ovarian cancer patients

Abstract Background: VTE is the leading cause of death in patients with cancer. The 1-year survival rate in patients diagnosed with cancer at time of VTE is 12% compared to 36% in cancer patients without thrombosis. Cancer patients who develop VTE have higher mortality during hospitalization and during surgery. VTE in cancer patients portends a poorer prognosis and may indicate a more aggressive phenotype. There are, as yet, no clinical or laboratory parameters that have clinical utility in identifying this important group of patients with cancer who are at risk for developing VTE. Methods: We explored whether gene expression profiling could define phenotype-specific metagenes (aggregate patterns of gene expression) that distinguish cancer patients with and without VTE. The medical history of 95 patients with NSCLC and 37 patients with ovarian cancer was reviewed to identify patients with VTE after the initial diagnosis of cancer but not within 6 weeks of surgery. Separate sets of controls with NSCLC and ovarian cancer, respectively, were identified from the same groups, matched by age, gender and clinical stage, but without VTE for at least 2 years following the diagnosis of cancer. RNA was extracted and gene array data obtained using Affymetrix U133 GeneChips. Gene expression data was analyzed using binary regression methodologies. Results: 13/95 (13.5%) patients with NSCLC and 6/37 (16%) with ovarian cancer had VTE and met inclusion criteria. Using the metagene approach, a discriminator gene set (n=50) that differentiated patients with NSCLC and VTE from patients with NSCLC without VTE was identified. A separate discriminator gene set was identified for the ovarian cancer group. A leave-one-out cross validation performed to validate the reliability of the discriminator metagene set was 85% accurate in identifying patients with NSCLC and VTE. Similar analysis for the ovarian cancer patients was limited by the small number of patients identified. Significant biological differences were seen between the comparison groups in the NSCLC subset, including genes such as P53, VEGFC, E2F4, TFPI and EPHB2. Expression differences in the ovarian subset similarly included P53, but also included genes not seen in the NSCLC group, such as H-ras, Tissue factor and Factor X. Conclusions: Our data suggests that a genomic approach can identify patients with cancer at risk for VTE. In addition, these results also suggest that different tumor types might possess unique expression signatures associated with increased thrombotic risk.

Download Full-text

Prognostic values of GMPS, PR, CD40, and p21 in ovarian cancer

PeerJ ◽

10.7717/peerj.6301 ◽

2019 ◽

Vol 7 ◽

pp. e6301 ◽

Cited By ~ 3

Author(s):

Ping Wang ◽

Zengli Zhang ◽

Yujie Ma ◽

Jun Lu ◽

Hu Zhao ◽

...

Keyword(s):

Gene Expression ◽

Ovarian Cancer ◽

Cancer Patients ◽

Differential Expression ◽

Cancer Progression ◽

Cancer Genome ◽

The Cancer Genome Atlas ◽

Progression Model ◽

Cancer Genome Atlas ◽

Genome Atlas

Early detection and prediction of prognosis and treatment responses are all the keys in improving survival of ovarian cancer patients. This study profiled an ovarian cancer progression model to identify prognostic biomarkers for ovarian cancer patients. Mouse ovarian surface epithelial cells (MOSECs) can undergo spontaneous malignant transformation in vitro cell culture. These were used as a model of ovarian cancer progression for alterations in gene expression and signaling detected using the Illumina HiSeq2000 Next-Generation Sequencing platform and bioinformatical analyses. The differential expression of four selected genes was identified using the gene expression profiling interaction analysis (http://gepia.cancer-pku.cn/) and then associated with survival in ovarian cancer patients using the Cancer Genome Atlas dataset and the online Kaplan–Meier Plotter (http://www.kmplot.com) data. The data showed 263 aberrantly expressed genes, including 182 up-regulated and 81 down-regulated genes between the early and late stages of tumor progression in MOSECs. The bioinformatic data revealed four genes (i.e., guanosine 5′-monophosphate synthase (GMPS), progesterone receptor (PR), CD40, and p21 (cyclin-dependent kinase inhibitor 1A)) to play an important role in ovarian cancer progression. Furthermore, the Cancer Genome Atlas dataset validated the differential expression of these four genes, which were associated with prognosis in ovarian cancer patients. In conclusion, this study profiled differentially expressed genes using the ovarian cancer progression model and identified four (i.e., GMPS, PR, CD40, and p21) as prognostic markers for ovarian cancer patients. Future studies of prospective patients could further verify the clinical usefulness of this four-gene signature.

Download Full-text

Glucocorticoid receptor expression is associated with inferior overall survival independent of BRCA mutation status in ovarian cancer

International Journal of Gynecological Cancer ◽

10.1136/ijgc-2018-000101 ◽

2019 ◽

Vol 29 (2) ◽

pp. 357-364 ◽

Cited By ~ 2

Author(s):

Jennifer Taylor Veneris ◽

Lei Huang ◽

Jane E Churpek ◽

Suzanne D Conzen ◽

Gini F Fleming

Keyword(s):

Gene Expression ◽

Ovarian Cancer ◽

Overall Survival ◽

Glucocorticoid Receptor ◽

Cancer Patients ◽

Brca Mutation ◽

Receptor Expression ◽

High Grade ◽

Mutation Status ◽

Relationship Of

ObjectiveHigh glucocorticoid receptor (GR) protein expression is associated with decreased progression-free survival in ovarian cancer patients and decreased sensitivity to chemotherapy in preclinical models. Prior studies suggest wild type BRCA1 promotes GR activation. The objective of this study was to characterize the relationship of tumor GR gene expression to outcome in ovarian cancer, and to evaluate the relationship of GR expression with BRCA status.MethodsWhole exome and whole genome sequencing, gene expression, and clinical data were obtained for high-grade serous ovarian cancers in The Cancer Genome Atlas. Cases with pathogenic somatic or germline BRCA1 or BRCA2 mutations were identified and classified as BRCA mutated. High or low glucocorticoid receptor expression was defined as expression above or below median of the GR/nuclear receptor subfamily 3 C1 (NR3C1) gene level. Overall survival was estimated by the Kaplan-Meier method and compared by Cox regression analysis.ResultsCombined germline DNA sequencing and tumor microarray expression data were available for 222 high-grade serous ovarian cancer cases. Among these, 47 had a deleterious germline and/or somatic mutation in BRCA1 or BRCA2. In multivariate analysis, high glucocorticoid receptor gene expression was associated with decreased overall survival among ovarian cancer patients, independently of BRCA mutation status. No correlation of GR/NR3C1 gene expression with BRCA mutation status or BRCA1 or BRCA2 mRNA level was observed.ConclusionsIncreased GR gene expression is associated with decreased overall survival in ovarian cancer patients, independently of BRCA mutation status. High-grade serous ovarian cancers with high GR expression and wild type BRCA have a particularly poor outcome.

Download Full-text

A novel six-gene signature for prognosis prediction in ovarian cancer

10.21203/rs.2.23976/v1 ◽

2020 ◽

Author(s):

Xin Pan ◽

Xiao-xin Ma

Keyword(s):

Gene Expression ◽

Ovarian Cancer ◽

Cancer Patients ◽

Gene Expression Signature ◽

Gene Signature ◽

Female Reproductive Tract ◽

Gene Set Enrichment Analysis ◽

Assessment Model ◽

Mesenchymal Transition ◽

Cox Regression Analysis

Abstract Ovarian cancer (OC) is the most malignant tumor in the female reproductive tract. Although abundant molecular biomarkers have been identified, a robust and accurate gene expression signature is still essential to assist oncologists in evaluating the prognosis of ovarian cancer patients. In the current study, 367 patients were adopted through The Cancer Genome Atlas (TCGA) database, and mRNA expression profiling was performed. Then, we used a gene set enrichment analysis (GSEA) to screen genes correlated with the epithelial-to-mesenchymal transition (EMT) process and identify these genes with the Cox proportional regression model. Six genes (TGFBI, SFRP1, COL16A1, THY1, PPIB, BGN) associated with overall survival (OS) were used to construct a risk assessment model, by which the patients were divided into high-risk and low-risk groups. The six-gene signature was identified as an independent prognostic biomarker of the OS of ovarian cancer patients via multivariate Cox regression analysis. Besides, the six-gene model was also validated significantly by Gene Expression Omnibus (GEO) database. In summary, we established a six-gene signature relevant to the prognosis of ovarian cancer, which might become a therapeutic target with clinical usefulness in the future.

Download Full-text

Development and validation of an immune-related prognostic signature for ovarian cancer based on weighted gene co-expression network analysis

10.21203/rs.3.rs-46925/v1 ◽

2020 ◽

Author(s):

Yuanyuan An ◽

Qing Yang

Keyword(s):

Gene Expression ◽

Ovarian Cancer ◽

Network Analysis ◽

Immune Responses ◽

Cancer Patients ◽

Cox Regression ◽

M2 Macrophage ◽

Prognostic Signature ◽

Cox Regression Analysis ◽

Geo Database

Abstract Background Ovarian cancer is one of the most lethal diseases of women. The prognosis of ovarian cancer patients was closely correlated with immune cell expression and immune responses. Therefore, it is important to identify a robust prognostic signature, which not only correlates with prognoses but also with immune responses in ovarian cancer, thus, providing immune-related patient therapies. Methods Weighted gene co-expression network analysis (WGCNA) was used to identify candidate genes correlated with ovarian cancer prognoses. Univariate and multivariate cox regression analyses were used to construct the prognostic signature. The Kaplan-Meier method was used to predict survival, and the immune-related bioinformatics analysis was performed using R software. The relationship between the signature and clinical parameters was analyzed with GraphPad Prism 7 and SPSS software. Results Gene expression from The Cancer Genome Atlas dataset was used to perform the WGCNA analysis, and identified candidate prognostic-related genes in patients with ovarian cancer. According to the Cox regression analysis, the prognostic signature was constructed, which divided patients into two groups. The high-risk group showed the least favorable prognosis. Three independent cohorts from the Gene Expression Omnibus (GEO) database were used for the validation studies. According to the immune analyses, the GEO database signatures were significantly correlated with the immune statuses of ovarian cancer patients. By analyzing the combination of the prognostic signature and total mutational burden (TMB), ovarian cancer patients were divided into four groups. In these groups, memory B cell, resting memory CD4 T cell, M2 macrophage, resting mast cell and neutrophil were found significant distinctions among these groups. Conclusions This novel signature predicted the prognosis of ovarian cancer patients precisely and independently and showed significant correlations with immune responses. Therefore, this prognostic signature might could indicate targeted immunotherapies for ovarian cancer patients.

Download Full-text

A machine learning approach to identify predictive molecular markers for cisplatin chemosensitivity following surgical resection in ovarian cancer

Scientific Reports ◽

10.1038/s41598-021-96072-6 ◽

2021 ◽

Vol 11 (1) ◽

Author(s):

Nicholas Brian Shannon ◽

Laura Ling Ying Tan ◽

Qiu Xuan Tan ◽

Joey Wee-Shan Tan ◽

Josephine Hendrikson ◽

...

Keyword(s):

Gene Expression ◽

Ovarian Cancer ◽

Molecular Markers ◽

Surgical Resection ◽

Clinical Information ◽

The Cancer Genome Atlas ◽

Validation Dataset ◽

Gradient Boosting ◽

Platinum Resistance ◽

Extreme Gradient Boosting

AbstractOvarian cancer is associated with poor prognosis. Platinum resistance contributes significantly to the high rate of tumour recurrence. We aimed to identify a set of molecular markers for predicting platinum sensitivity. A signature predicting cisplatin sensitivity was generated using the Genomics of Drug Sensitivity in Cancer and The Cancer Genome Atlas databases. Four potential biomarkers (CYTH3, GALNT3, S100A14, and ERI1) were identified and optimized for immunohistochemistry (IHC). Validation was performed on a cohort of patients (n = 50) treated with surgical resection followed by adjuvant carboplatin. Predictive models were established to predict chemosensitivity. The four biomarkers were also assessed for their ability to prognosticate overall survival in three ovarian cancer microarray expression datasets from The Gene Expression Omnibus. The extreme gradient boosting (XGBoost) algorithm was selected for the final model to validate the accuracy in an independent validation dataset (n = 10). CYTH3 and S100A14, followed by nodal stage, were the features with the greatest importance. The four gene signature had comparable prognostication as clinical information for two-year survival. Assessment of tumour biology by means of gene expression can serve as an adjunct for prediction of chemosensitivity and prognostication. Potentially, the assessment of molecular markers alongside clinical information offers a chance to further optimise therapeutic decision making.

Download Full-text